Challenges and Opportunities for Innovation in the Public Works ...

Sharing Experience Between Manufacturing and Construction
often faced with large amounts of information obtained from simulations, experiments, and past
solutions. Given this information, they must construct know-how models which are optimal with
respect to the specific problems at hand. The knowledge required for constructing optimal models
and refining them to enhance performance is called know-what knowledge. This knowledge is
necessary to support the various stages of the decisions, and to cope with the changing
perspectives in concurrent engineering. A new modeling methodology for interactive model
formation and utilization to support know-what knowledge processing was developed (27). The
methodology integrates simulation, learning, and optimization to form an Adaptive and Interactive
Modeling System (AIMS) to support multiple perspectives (R-b-) at different stages (R-c-).
Given a databa,e of examples collected from simulations or experiments (R-d-l), and a set of
modeling objectives and design objectives, AIMS performs multi-objective optimization at both
the model formation and utilization phases. During model formation, layered models with
explicit trade-offs between modeling objectives are induced from the example database. These
models are then used during model utilization to find optimal solutions with explicit trade-offs
between design objectives. By varying modeling objectives, such as speed, accuracy, and
comprehensibility, AIMS can produce layered models to meet the changing needs at different
stages of design (R-c-2). By treating different life-cycle concerns as competing design objectives.
AIMS can support systematic incorporation of multiple perspectives for concurrent engineering.
Furthermore, a layered set of models built by AIMS offers several distinct advantages over
traditional analysis models which can only provide evaluations at very detailed stages of product
development. These advantages include early evaluation to avoid costly iterations (R-c-3), fast
execution for interactive decision making, enhanced comprehensibility for human inspection (Rb-4),
and deep roots in domain physics for higher accuracy. AIMS represents an intelligent
integration between Al and traditional engineering methods to form the needed know-what KPT.
6.7 Integration and Application of KPTs for Concurrent Engineering
The above core KPT tools offer a set of desirable functions which have been integrated and
applied to provide concurrent engineering solutions. Some of these application and integration
efforts performed at KBESRL (see Figure 7) are briefly summarized below. It should be noted
that although not all the current applications are in the specific context of product development,
all systems demonstrate some functions required by concurrent engineering. Also, due to the
space limitation, only one reference paper is cited for each system. Those readers who are
interested in more details of these syst,-rns should refer to (19).
TOPS Turning Operation Planning System: a domain specific application system built by IDEEA
for process planning of lathe-turned parts (28). Although not directly used in the concurrent
engineering context, TOPS serves as a critical link for design and manufacturing integration (Ra-I).
The system is linked with a solid modeler for design and a relational database for tooling
(R-a-4).
CAPP-EP Computer-aided Process Planning Enabling Platform: IDEEA is used to implement
a set of generic function modules, called the enabling platform, for developing different process
planning systems (29). The system adapts to the current PDES standards (R-a-3), and has links
to CAD tools and databases (R-a-4). In addition to functional supports to CAPP systems, our
CAPP-EP can record plan intents (R-b-1) and maintain consistency for planning activities (R-
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